Method and system for image processing for structured light profiling of a part

ABSTRACT

An image processing method for structured light profiling includes sampling an image of a structured light pattern to obtain an intensity distribution, selecting a number of sets of sampled points from the intensity distribution. Each of the respective sets includes a number of sampled points. The image processing method further includes fitting each of the sets of sampled points to a respective distribution function and filtering the distribution functions to select a representative distribution function for the intensity distribution.

BACKGROUND OF THE INVENTION

The present invention relates generally to structured light profiling ofa part and more specifically to methods and systems for image processingto obtain a three dimensional profile of a part using a structured lightsource.

In structured light applications, a three dimensional profile of a partis obtained by using a laser source and at least one video camera. Alaser source emanates a laser beam and spreads into multiple (one ormore) laser stripes which strikes a surface of a part. These stripes areviewed from one or more video cameras from an angle other than theillumination angle. Typically, applications for structured lightprofiling include obtaining shape profile information about the part orgenerating a 3D contour map of the part.

The limitations of using laser stripes to obtain accurate profileinformation are mainly attributed to sampling error and the noiseassociated with the laser because the center of a laser stripe may notbe imaged at the center of the pixel of the camera and may not be thedetected intensity peak. Sampling error occurs while locating the centerof the laser stripe on the image. There are image processing techniquessuch as maximum intensity, intensity center, Gaussian fitting andzero-crossing which attempt to extract the relevant information from thelaser stripe. The associated problem with several of these techniques isthat it gives the location of the highest peak, which is not the truecenter of the stripe. Current correction techniques for this errorinclude neighborhood averaging over neighboring pixels and doing aweighted average or using fitting methods, but these techniques alsofail to adequately address the sampling error.

The noise associated with the laser primarily takes the form of laserspeckle, which is the oscillation of the intensity profile for a laserwhen it is reflected from the surface of the part and is caused bycoherency of the laser. One way to reduce the speckle noise is bychoosing an appropriate viewing system. By changing the size of theaperture, the size of the speckle changes, the larger the aperture, thesmaller the size of the speckle. However, in this case there's a depthof field tradeoff.

Therefore there is need for an improved image processing technique forreducing the speckle noise and also the sampling error in structuredlight applications.

BRIEF DESCRIPTION OF THE INVENTION

Briefly, in accordance with one aspect of the present invention, animage processing method for structured light profiling includes samplingan image of a structured light pattern to obtain an intensitydistribution. A number of sets of sampled points are then selected fromthe intensity distribution, where each of the respective sets includes anumber of sampled points. Each of the sets of sampled points is thenfitted to a respective distribution function. Finally, the distributionfunctions are filtered to select a representative distribution functionfor the intensity distribution.

In accordance with another aspect of the present invention, a system forobtaining a three dimensional profile of a part using a structured lightpattern includes a source of structured light positioned at apredetermined distance from a part, where the source projects a beam ofstructured light to illuminate the part. The system also includes atleast one imaging device configured to acquire an image of a structuredlight pattern of the part, where the at least one imaging device ispositioned such that an angle of view of the imaging device is differentfrom an angle of illumination of the source. A processor is coupled tothe at least one imaging device, and the processor is configured for avariety of tasks including sampling the image of a structured lightpattern to obtain an intensity distribution; selecting a number of setsof sampled points from the intensity distribution, where each of thesets includes at least three sampled points; fitting each of the sets ofsampled points to respective Gaussian distribution functions; extractinga center for each of the Gaussian distribution functions; filtering theGaussian distribution functions by using the centers to select arepresentative distribution function for the intensity distribution; andreconstructing a three dimensional profile of the part, using therepresentative distribution function.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 illustrates a system for obtaining a three dimensional profile ofa part using a structured light source;

FIG. 2 illustrates a set of graphs for three different orientations of acurved surface being viewed by two cameras;

FIG. 3 is a graphical representation of a number of sampling points atthree different locations of an intensity distribution;

FIG. 4 is a graphical representation of sampled intensity profiles forthe three locations illustrated in FIG. 3;

FIG. 5 is a graphical representation of a sampled intensity profile withspeckle noise and sampling error;

FIG. 6 is a flowchart that illustrates one aspect of an image processingmethod for structured light profiling for use in the system of FIG. 1;

FIG. 7 illustrates a flowchart for determining the position of laserstripe by using a center of the representative distribution functionaccording to an aspect of the image processing technique; and

FIG. 8 illustrates a flowchart for determining the position of laserstripe by using centers of distribution functions according to an aspectof the image processing method.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a system 10 for obtaining a three dimensional profileof a part 16 using a structured light pattern 18. The system includes asource 12 of structured light 14 positioned at a predetermined distancefrom a part. The source 12 projects a beam of structured light 14 toilluminate the part 16. In the illustrated embodiment, a laser is usedas the structured light source 14. However other mediums like whitelight may also be used. At least one imaging device 20 is configured toacquire an image 28 of a structured light pattern of the part 16 byviewing along the direction 22. The imaging device 20 is positioned suchthat an angle of view (α) of the imaging device 20 is different from anangle of illumination (β) of the source 12. A processor 26 is coupledthrough 24 to the imaging device 20 and is configured for reconstructingthe three dimensional profile of the part using the methods describedhereinbelow.

As would be appreciated by those skilled in the art, the intensityprofile for a flat surface in the case of illumination with Gaussianprofile by source 12 and any viewing angle of the imaging device 20, forany tilt angle (θ) of the surface of the part 16, remains a Gaussiandistribution. For such a symmetric intensity profile on a flat surface,it will be well appreciated by those skilled in the art that sampling ofthe symmetric point and applying zero-crossing which is a secondderivative on the profile, will yield the center corresponding to themaximum intensity.

However, in the case of a complex part, where the surface is not flat,the intensity profile is no longer symmetric. In case of a curvedsurface, the curvature of the surface may be approximated by a cylinderto analyze the intensity profiles for a Gaussian distribution. It isfound that the degree of asymmetry δ (the maximum distance differencebetween two points with the same intensity to the maximum intensitypoint) is small and hence the effect on image due to this asymmetry issmall. Therefore the intensity profile near a cylindrical neighborhoodin a curved surface can also be assumed to be a Gaussian distribution.In this case, considering that the maximum intensity point is sampled,the image bias after applying zero-crossing is also small, of the orderof 10⁻². This is shown by a set of graphs in FIG. 2, for three differentorientations of a curved surface at angles −40 degrees, 0 degrees and 40degrees being viewed by two cameras, with the views being depicted as aleft view and a right view respectively. Also in FIG. 2, {right arrowover (V)} is the viewing vector unit determined by viewing angle α,{right arrow over (N)} is the surface normal unit determined by surfacetilt angle θ and {right arrow over (B)} is the vector unit of the beamcenter line determined by beam divergent angle β. The degree ofasymmetry δ is shown in FIG. 2 for the left and right views fordifferent tilt angles of the curved surface.

However, if the maximum intensity point is not sampled, i.e. there issampling error, zero-crossing does not give accurate results and leadsto a larger image bias as shown by the set of graphical representationsin FIG. 3 and FIG. 4. FIG. 3 depicts a number of sampling points 43 atthree different locations 44, 46, 48 of an intensity distribution 36.FIG. 4 depicts the sampled intensity profiles 50, 52 and 54 for thelocations 44, 46 and 48 respectively. These profiles 52 and 54 (as isclear from FIG. 4) are not a true representation of the intensityprofile 36 due to the sampling error. Further, speckle also leads toenhanced image bias error, as it causes the intensity profile to changeas shown in FIG. 5. Therefore, if there is speckle noise, the intensityprofile 36 of FIG. 4 will appear as the sampled intensity profile 58with sampling error and added speckle noise at 56.

In order to reduce sampling error and speckle noise, an imagingprocessing method is provided. FIG. 6 illustrates one aspect of an imageprocessing method for structured light profiling for use in the system10 of FIG. 1. The method includes acquiring the image of the structuredlight pattern of a part. The structured light pattern includes at leastone laser stripe, and the intensity distribution is an intensity profileacross one of the laser stripes. Referring to the flowchart of FIG. 6,the method starts at 60 with an image 28, which is sampled to obtain anintensity distribution of the structured light pattern at step 62. Next,at step 64, a number of sets of sampled points are selected from theintensity distribution, where each of the respective sets includes anumber of sampled points. In one example each of the sets of sampledpoints includes at least three sampled points. In Step 66 each of thesets of sampled points is fit to a respective distribution function. Inone example each of the distribution functions is a Gaussiandistribution. It is found that fitting the intensity profile for acurved surface with a Gaussian distribution leads to reducing samplingerror. In Step 68 the distribution functions are filtered to select arepresentative distribution function with reduced effect of specklenoise for the intensity distribution. The method concludes at 69 with anoutput of the representative distribution function, which is furtherprocessed to reconstruct the three dimensional profile of the part.

FIG. 7 illustrates additional aspects of the image processing method.For this embodiment, the image processing method also includesextracting a center of the representative distribution function, whichis shown as step 72 in FIG. 7. The input 70 to the process illustratedin FIG. 7 is the representative distribution function, and the output 74is the center of the representative distribution function depicting theposition of the laser stripe on the complex part. The center of therepresentative distribution function may be extracted, for example,using zero-crossing. The exemplary fitting method, according to anaspect of the present technique, fits sets of three sampled points torespective distribution functions, in order to calculate the center ofthe representative distribution function, as mentioned above. As wouldbe appreciated by one skilled in the art, more than three sampled pointscan be used as well.

FIG. 8 illustrates another embodiment of the image processing method.For this embodiment, a center is extracted for each of the distributionfunctions as shown in step 82 of FIG. 8. The centers may be extractedusing zero-crossing. The input 80 to this process will be thedistribution functions, which are generated at step 66 in FIG. 6. Thefiltering step includes using these centers in the subsequent step 84.For example, multiple centers may be calculated using different sets ofthree points from the intensity profile. If the multiple centers areclose, the average or median can be used as a fitted center of theGaussian distribution. Namely, for this example, the filtering stepincludes selecting a median of the centers, and the representativedistribution function corresponds to the median of the centers. Ahistogram may be used to determine the median. Alternatively, thecenters may be sorted to determine the median. As will be appreciated byone skilled in the art, these filtering techniques select therepresentative center for the distribution functions that depicts theposition of laser stripe center on the complex part.

In case there is a speckle which can be observed as an outlier on theintensity profile, the corresponding center using the outlier can beeasily discarded and the speckle noise can be addressed. This isindicated in FIG. 8, in which step 84 of filtering includes rejecting anumber of distribution functions that involve outliers. A filteringexample is discussed with reference to FIG. 5. As shown in FIG. 5, thereis an added speckle at sampling point 4 (total 9 points) and thecalculated seven centers are, 0, −6.06, 0.16, −0.32, 0, 0, 0. For thisfiltering example, a closest group amongst the calculated centers isselected and zero is selected. The statistical analysis methods canfurther use histograms to screen speckle noise using the median value.Beneficially, this fitting requires only three sampled points at a timeto calculate multiple values for the centers which can be used forfiltering without compromising computation speed.

As noted above, the flowcharts illustrated in FIG. 6, FIG. 7, and FIG. 8describe the aspects of the method discussed hereinabove. The foregoingflow charts also show the functionality and operation of the method andthe system for reconstructing a three dimensional profile of a part. Inthis regard, each block/component represents a module, segment, orportion of code, which comprises one or more executable instructions forimplementing the specified logical functions. It should also be notedthat in some alternative implementations, the functions noted in theblocks may occur out of the order noted in the figures or, for example,may in fact be executed substantially concurrently or in the reverseorder, depending upon the functionality involved. Also, one of ordinaryskill in the art will recognize that additional blocks may be added.Furthermore, the functions can be implemented in programming languagessuch as C++ or JAVA; however, other languages can be used.

The various embodiments and aspects of the invention described abovecomprise an ordered listing of executable instructions for implementinglogical functions. The ordered listing can be embodied in anycomputer-readable medium for use by or in connection with acomputer-based system that can retrieve the instructions and executethem. In the context of this application, the computer-readable mediumcan be any means that can contain, store, communicate, propagate,transmit or transport the instructions. The computer readable medium canbe an electronic, a magnetic, an optical, an electromagnetic, or aninfrared system, apparatus, or device. An illustrative, butnon-exhaustive list of computer-readable mediums can include anelectrical connection (electronic) having one or more wires, a portablecomputer diskette (magnetic), a random access memory (RAM) (magnetic), aread-only memory (ROM) (magnetic), an erasable programmable read-onlymemory (EPROM or Flash memory) (magnetic), an optical fiber (optical),and a portable compact disc read-only memory (CDROM) (optical).

Note that the computer readable medium may comprise paper or anothersuitable medium upon which the instructions are printed. For instance,the instructions can be electronically captured via optical scanning ofthe paper or other medium, then compiled, interpreted or otherwiseprocessed in a suitable manner if necessary, and then stored in acomputer memory.

The various aspects of the technique described hereinabove have utilityin industrial as well as medical environments. The methods can be usedfor non-contact measurement of complex parts e.g. aircraft parts forinspection, in the extrusion process in the steel industry and otherhigh temperature manufacturing environments where contact measurement ofa part is difficult. These methods are also useful in medical fields forsurgery planning, where these may be used for profiling the differentparts of a human body to have precision in surgery.

Although only certain features of the invention have been illustratedand described herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

1. An image processing method for structured light profiling, saidmethod comprising: sampling an image of a structured light pattern toobtain an intensity distribution; selecting a plurality of sets ofsampled points from the intensity distribution, wherein each of therespective sets comprises a plurality of sampled points; fitting each ofthe sets of sampled points to a respective Gaussian distributionfunction; and filtering the Gaussian distribution functions to select arepresentative distribution function for the intensity distribution. 2.The image processing method of claim 1, further comprising extracting acenter of the representative distribution function.
 3. The imageprocessing method of claim 2, wherein said extracting the centercomprises using zero-crossing.
 4. The image processing method of claim1, wherein each of the sets of sampled points comprises at least threesampled points.
 5. The image processing method of claim 1, wherein eachof the distribution functions comprises a Gaussian distribution.
 6. Theimage processing method of claim 1, further comprising extracting arespective center for each of the distribution functions, wherein saidfiltering includes using the centers.
 7. The image processing method ofclaim 6, wherein said extracting comprises using zero-crossing.
 8. Theimage processing method of claim 6, wherein said filtering includesrejecting a plurality of outliers.
 9. The image processing method ofclaim 6, wherein said filtering includes selecting a median of thecenters, wherein the representative distribution function corresponds tothe median.
 10. The image processing method of claim 9, wherein saidfiltering further includes using a histogram to determine the median.11. The image processing method of claim 9, wherein said filteringfurther includes sorting the centers to determine the median.
 12. Theimage processing method of claim 1, wherein said structured lightpattern comprises at least one laser stripe, and wherein said intensitydistribution is an intensity profile across one of the laser stripes.13. The image processing method of claim 1, further comprising acquiringthe image of the structured light pattern of a part.
 14. The imageprocessing method of claim 13, further comprising reconstructing a threedimensional profile of the part, using the representative distributionfunction.
 15. An image processing method for structured light profiling,said method comprising: sampling an image of a structured light patternto obtain an intensity distribution; selecting a plurality of sets ofsampled points from the intensity distribution, wherein each of the setscomprises at least three sampled points; fitting each of the sets ofsampled points to respective Gaussian distribution functions; extractinga center for each of the Gaussian distribution functions; filtering theGaussian distribution functions by using the centers to select arepresentative distribution function for the intensity distribution. 16.The image processing method of claim 15, wherein said extractingcomprises using zero-crossing.
 17. The image processing method of claim15, wherein said filtering includes selecting a median of the centers,wherein the representative distribution function corresponds to themedian.
 18. An image processing method for reconstructing a threedimensional profile of a part, said method comprising: projecting atleast one laser beam on the part; acquiring an image of a structuredlight pattern of the part; sampling the image to obtain an intensitydistribution; selecting a plurality of sets of sampled points from theintensity distribution, wherein each of the sets comprises at leastthree sampled points; fitting each of the sets of sampled points to arespective Gaussian distribution function; extracting a center for eachof the Gaussian distribution functions; filtering the Gaussiandistribution functions by using the centers to select a representativedistribution function for the intensity distribution; and using therepresentative distribution function for the intensity distribution togenerate the three dimensional profile of the part.
 19. The imageprocessing method of claim 18, wherein said filtering includes selectinga median of the centers, wherein the representative distributionfunction corresponds to the median.
 20. A system for obtaining a threedimensional profile of a part using a structured light pattern, saidsystem comprising: a source of structured light positioned at apredetermined distance from a part, wherein the source projects a beamof structured light to illuminate the part; at least one imaging deviceconfigured to acquire an image of a structured light pattern of thepart, wherein the at least one imaging device is positioned such that anangle of view of the imaging device is different from an angle ofillumination of the source; and a processor coupled to the at least oneimaging device, wherein the processor is configured for sampling theimage of a structured light pattern to obtain an intensity distribution;selecting a plurality of sets of sampled points from the intensitydistribution, and wherein each of the sets comprises at least threesampled points; fitting each of the sets of sampled points to respectiveGaussian distribution functions; extracting a center for each of theGaussian distribution functions; filtering the Gaussian distributionfunctions by using the centers to select a representative distributionfunction for the intensity distribution; and reconstructing a threedimensional profile of the part, using the representative distributionfunction.
 21. The system of claim 20, wherein said processor is furtherconfigured for selecting a median of the respective centers, wherein therepresentative distribution function corresponds to the median.
 22. Acomputer readable medium for storing and/or transmitting instructionsthat, when executed by a computer, perform a method for image processingfor structured light profiling, said method comprising: sampling animage of a structured light pattern to obtain an intensity distribution;selecting a plurality of sets of sampled points from the intensitydistribution, wherein each of the sets comprises a plurality of sampledpoints; fitting each of the sets of sampled points to a respectivedistribution function; and filtering the distribution functions toselect a representative distribution function for the intensitydistribution.
 23. The computer readable medium of claim 22, wherein eachof the sets of sampled points comprises at least three sampled points.24. The computer readable medium of claim 22, wherein each of thedistribution functions comprises a Gaussian distribution.
 25. Thecomputer readable medium of claim 22, wherein the method furtherincludes extracting a respective center for each of the distributionfunctions, wherein said filtering includes using the centers.
 26. Thecomputer readable medium of claim 25, wherein the extracting comprisesusing zero-crossing.
 27. The computer readable medium of claim 25,wherein the filtering includes selecting a median of the centers,wherein the representative distribution function corresponds to themedian.